The annual cost to society of substance use disorders (SUDs) is estimated at over $600 billion per year. Tobacco use alone is estimated to cost the U.S. over $193 billion a year, and it is the leading cause of preventable death worldwide. Despite the enormous toll of SUDs, knowledge about the addicted human brain is still minimal, and pharmacotherapies are inadequate. Our lab is currently conducting a series of large studies on the genetics of SUDs, and subject collection continues. We propose to use our carefully phenotyped sample to achieve a better understanding of how genetic variation influences different aspects of substance use, incorporating computational approaches that integrate existing and newly generated genomic, epigenomic, and transcriptomic datasets. Specific aims: Three independent aims are included in this proposal, each of which uses a focused strategy to maximize the value of our large, and still growing, dataset of over 10,000 subjects. For Aim 1, we will examine the molecular genetic contributions to withdrawal phenotypes. Aim 1 is enhanced by the inclusion of exome array data from > 8,000 subjects. Aim 2 focuses on identifying which nominally significant single nucleotide polymorphisms (SNPs) for the well-established cigarettes per day (CPD) phenotype represent real biological signals. Multilevel 'omics' data from human brains will be used to identify functional SNPs. These high priority loci can then be tested for an enrichment of CPD genome-wide association study (GWAS) signals, and assessed to determine which CPD associations replicate in large meta-analyses. We have already successfully applied this method in the context of alcohol dependence. Lastly, for Aim 3 we have collected peripheral blood gene expression data from human subjects participating in an acute intravenous nicotine administration laboratory paradigm. We will identify modules of co-expressed genes that are regulated in response to nicotine. Biologically relevant modules can then be identified by testing modules for overrepresentation of genes highlighted by GWAS.